In order to solve the problem that network intrusion detection was weak in training speed, real-time process and high false positive rate when dealing with big data, a Dendritic Cell TWin Support Vector Machine (DCTWSVM) approach was proposed. The Dendritic Cell Algorithm (DCA) was firstly used for the basic intrusion detection, and then the TWin Support Vector Machine (TWSVM) was applied to optimize the first step detection outcome. The experiments were carried out for testing the performance of the approach. The experimental results show that DCTWSVM respectively improves the detection accuracy by 2.02%, 2.30%, and 5.44% compared with DCA, Support Vector Machine (SVM) and Back Propagation (BP) neural network, and reduces the false positive rate by 0.26%, 0.46%, and 0.90%. The training speed is approximately twice as the SVM, and the brief training time is another advantage. The results indicate that the DCTWSVM is suitable for the comprehensive intrusion detection environment and helpful to the real-time intrusion process.

Combined with virtualization technology, a realization scheme of private cloud platform based on multi-framework technology was put forward in order to improve hardware utilization of distributed cluster system and avoid the economic loss caused by the idle equipment. This scheme met the demand for resource allocation, dynamic allocation and dynamic migration, thus the bottom hardware was integrated. Aiming at unbalanced loading in the traditional virtual machine deployment method, the virtual machine deployment mechanism based on dynamic deployment allocation was proposed. According to the characteristics of virtual machine resources and the load of the current physical nodes, dynamic deployment of virtual machine was carried out. At last, private cloud computing platform with strong flexibility and good scalable performance was realized, which had been tested by Fourier finite difference pre-stack depth migration in oil exploration. The results proved the feasibility and effectiveness of the private cloud platform. By deployment test on virtual machine, the results show that the dynamic allocation decision can be used to deploy a large number of virtual machines, and keep good load balancing of private cloud platform at the same time.

Electronic Paper Display (EPD) can exhibit good comfortability in reading, but it has a critical drawback - slow refresh, which will be overcome by optimizing the design of the display's driver software. A tri-buffer-based architecture as well as its design methodology for the EPD driver software was proposed in this paper. Also an e-reader integrating the EPD driver in it was implemented to verify the design. Compared with the traditional dual-buffer architecture, the proposed tri-buffer scheme set an additional memory area to keep the EPD data frame. Test results show that the driver software works well in a real device without screen flicker and can help the display to achieve excellent performance.